MA4K8s: Machine advice for GitOps-managed Kubernetes configuration optimisation
At a glance
- Project leader : Dr. Josef Spillner
- Co-project leader : Panagiotis Gkikopoulos
- Project status : completed
- Funding partner : Innosuisse (Innovationsscheck / Projekt Nr. 63912.1 INNO-ICT)
- Project partner : VSHN AG
- Contact person : Josef Spillner
Description
The profitability of cloud providers is often negatively affected by misconfiguration of application resource constraints. In this research study, we check the feasibility of integrating ML on usage-dependent configurations into a GitOps workflow. The result will be a novel advisor service that tells GitOps engineers about monetary implications of detected misconfigurations.
Associated research questions are: Can existing ML components be integrated into GitOps workflows? Are they scalable enough to process metrics from a growing customer base? Do they identify the parts of the configuration that are the low-hanging fruits in the sense of saving most resource expenses after adopting few suggested changes?